Traffic Flow Management Using Aggregate Flow Models and the Development of Disaggregation Methods

Sun, Dong; Sridhar, Banavar; Grabbe, Shon

doi:10.2514/6.2009-6007

Cited by 14 publications

(8 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several NASA efforts have investigated TFM using advanced iterative algorithms not only for strategic TFM in the National Airspace System (NAS) but also for managing surface traffic flows [1][2][3][4][5][6][7][8]. While these algorithms can provide precise solutions to the traffic management problem, they are more suitable for predictive control based on deterministic data.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Air Traffic Flow Estimation in the Terminal Area

Yang

Menon

2015

Journal of Aircraft

View full text Add to dashboard Cite

The problem of estimating the characteristics of the air traffic flow in the terminal area is considered. The estimated parameters can be used by the traffic flow coordinators and terminal area controllers to improve their response to varying air traffic flow. The approach is based on a queuing abstraction of the arrival and departure traffic routes in the terminal area. The routes are discretized as spatial servers for enforcing Federal Aviation Administration mandated separation requirements. Particle filtering methodology is employed for estimating the time-varying queuing parameters using radar track data. By considering the waypoint crossing times and airspeed as measurements, it is shown that it is feasible to estimate the time delay and its rate at various points along the arrival/departure routes. The proposed approach is illustrated using historical radar track data. A graphic display of estimated parameters to serve as a decisionsupport tool for use by the terminal area traffic controllers is also illustrated.

show abstract

Section: Introductionmentioning

confidence: 99%

Real-Time Air Traffic Flow Estimation in the Terminal Area

Yang

Menon

2015

Journal of Aircraft

View full text Add to dashboard Cite

show abstract

“…ECISION Support Tools (DSTs) for Traffic Flow Management (TFM) need the characterization of the current and future traffic situations for effective management. Several NASA efforts have investigated TFM using advanced iterative algorithms not only for strategic TFM in the National Airspace System (NAS) but also for managing surface traffic flows [1][2][3][4][5][6][7][8] . While these algorithms can provide precise solutions to the traffic management problem, they are more suitable for predictive control based on deterministic data.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Air Traffic Flow Estimation for Improved Situational Awareness in the Terminal Area

Yang

Menon

2013

2013 Aviation Technology, Integration, and Operations Conference

View full text Add to dashboard Cite

The problem of estimating the characteristics of the air traffic flow in the Terminal Area is considered. The estimated parameters can be used by the Traffic Flow Coordinators and Terminal Area Controllers to improve their response to varying air traffic flow. The approach is based on a queuing abstraction of the arrival and departure traffic routes in the Terminal Area. The routes are discretized as spatial servers for enforcing FAA-mandated separation requirements. Particle filtering methodology is employed for estimating the timevarying queuing parameters using radar track data. By considering the waypoint crossing times and airspeed as measurements, it is shown that it is feasible to estimate the time delay and its rate at various points along the arrival-departure routes. The proposed approach is illustrated using arrival radar track data in the San Francisco Metroplex. A graphic display of estimated parameters to serve as decision support tool for use by the Terminal Area traffic controllers is also illustrated.

show abstract

“…everal research efforts are underway at NASA on air traffic flow management (TFM) using advanced iterative numerical algorithms [1][2][3][4][5][6][7][8] . These algorithms are being considered not only for strategic TFM in the National Airspace System (NAS), but also in managing surface traffic flows.…”

Section: Introductionmentioning

confidence: 99%

Air Traffic Estimation and Decision Support for Stochastic Flow Management

Sengupta

Tandale

Cheng

et al. 2011

AIAA Guidance, Navigation, and Control Conference

View full text Add to dashboard Cite

Development of a decision support system that uses real-time track data to estimate statistical parameters describing the stochastic traffic flow, and then applies modern statistical decision theory to optimize traffic flow is described. An advanced estimation algorithm provides the parameter estimates based on queuing network models of traffic flow. A hypothesis testing approach is developed for triggering traffic flow management initiatives in the terminal area, and a stochastic quadratic programming methodology is advanced to achieve flow control objectives such as runway load balancing. The use of this methodology is demonstrated using multi-day track data from the San Francisco terminal area. It is shown that the methodology can correctly identify the need for restricting the traffic flow into the terminal area, and provide decision support to balance the traffic flow at the runways under uncertain traffic flow conditions. The approach developed in this research can be extended to create decision support tools for a wide variety of stochastic air traffic flow control situations.

show abstract

Traffic Flow Management Using Aggregate Flow Models and the Development of Disaggregation Methods

Cited by 14 publications

References 13 publications

Real-Time Air Traffic Flow Estimation in the Terminal Area

Real-Time Air Traffic Flow Estimation in the Terminal Area

Real-Time Air Traffic Flow Estimation for Improved Situational Awareness in the Terminal Area

Air Traffic Estimation and Decision Support for Stochastic Flow Management

Contact Info

Product

Resources

About